
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 					lowerCamelCase__				(							__lowercase	,		unittest.TestCase):




	'''simple docstring'''




	_A								=	CTRLTokenizer
	_A								=	False
	_A								=	False







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								__UpperCamelCase      :  Any       =   ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
								__UpperCamelCase      :  int       =   dict(zip(a	,  range(len(a		)		)		)		)
								__UpperCamelCase      :  str       =   ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
								__UpperCamelCase      :  List[str]       =   {"unk_token": "<unk>"}

								__UpperCamelCase      :  int       =   os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]		)
								__UpperCamelCase      :  Any       =   os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["merges_file"]		)
								with open(self.vocab_file	,  "w"	,  encoding="utf-8"		) as fp:
															fp.write(json.dumps(a		) + "\n"		)
								with open(self.merges_file	,  "w"	,  encoding="utf-8"		) as fp:
															fp.write("\n".join(a		)		)







	def   _lowerCamelCase							(   self						:Optional[Any]	,  **a						:Any		)						->					List[str]:
								kwargs.update(self.special_tokens_map		)
								return CTRLTokenizer.from_pretrained(self.tmpdirname	,  **a		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:Optional[Any]		)						->					Optional[int]:
								__UpperCamelCase      :  List[Any]       =   "adapt react readapt apt"
								__UpperCamelCase      :  str       =   "adapt react readapt apt"
								return input_text, output_text







	def   _lowerCamelCase							(   self						:Tuple		)						->					Optional[Any]:
								__UpperCamelCase      :  int       =   CTRLTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map		)
								__UpperCamelCase      :  int       =   "adapt react readapt apt"
								__UpperCamelCase      :  str       =   "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
								__UpperCamelCase      :  Tuple       =   tokenizer.tokenize(a		)
								self.assertListEqual(a	,  a		)

								__UpperCamelCase      :  Union[str, Any]       =   tokens + [tokenizer.unk_token]

								__UpperCamelCase      :  Optional[int]       =   [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a		)	,  a		)

from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:Tuple	,  a						:float		)						->					float:
								return 0.0




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     np.ndarray		,    _lowerCamelCase					:     int)   ->       tuple[int | float, int | float]:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   min([-20, np.min(fft_results[1 : samplerate // 2 - 1])])
							__UpperCamelCase      :  Any       =   max([20, np.max(fft_results[1 : samplerate // 2 - 1])])
							return lowest, highest




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  List[str]       =   512
							__UpperCamelCase      :  List[Any]       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  List[Any]       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Optional[Any]       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.abs(np.fft.fft(_lowerCamelCase))
							__UpperCamelCase      :  Optional[int]       =   20 * np.logaa(_lowerCamelCase)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							# Display within reasonable bounds
							__UpperCamelCase      :  Optional[Any]       =   get_bounds(_lowerCamelCase		,    _lowerCamelCase)
							plt.ylim(max([-80, bounds[0]])		,    min([80, bounds[1]]))
							plt.ylabel("Gain (dB)")

							plt.plot(_lowerCamelCase)
							plt.show()




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     FilterType		,    _lowerCamelCase					:     int)   ->       None:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   512
							__UpperCamelCase      :  Dict       =   [1] + [0] * (size - 1)
							__UpperCamelCase      :  Tuple       =   [filter_type.process(_lowerCamelCase) for item in inputs]

							__UpperCamelCase      :  Dict       =   [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__UpperCamelCase      :  Optional[int]       =   np.angle(np.fft.fft(_lowerCamelCase))

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24		,    samplerate / 2 - 1)
							plt.xlabel("Frequency (Hz)")
							plt.xscale("log")

							plt.ylim(-2 * pi		,    2 * pi)
							plt.ylabel("Phase shift (Radians)")
							plt.plot(np.unwrap(_lowerCamelCase		,    -2 * pi))
							plt.show()